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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Euclidean Affine Functions and Applications to Calendar A...
Cassio Neri, Lorenz Schneider · 2021-02-14 · via cs.DS updates on arXiv.org

We study properties of Euclidean affine functions (EAFs), namely those of the form $f(r) = (α\cdot r + β)/δ$, and their closely related expression $\mathring{f}(r) = (α\cdot r + β)\%δ$, where $r$, $α$, $β$ and $δ$ are integers, and where $/$ and $\%$ respectively denote the quotient and remainder of Euclidean division. We derive algebraic relations and numerical approximations that are important for the efficient evaluation of these expressions in modern CPUs. Since simple division and remainder are particular cases of EAFs (when $α= 1$ and $β= 0$), the optimisations proposed in this paper can also be appplied to them. Such expressions appear in some of the most common tasks in any computer system, such as printing numbers, times and dates. We use calendar calculations as the main application example because it is richer with respect to the number of EAFs employed. Specifically, the main application presented in this article relates to Gregorian calendar algorithms. We will show how they can be implemented substantially more efficiently than is currently the case in widely used C, C++, C# and Java open source libraries. Gains in speed of a factor of two or more are common.